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Pulse rhythm

Pulse rhythm

Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...

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Related Experiment Video

Updated: May 5, 2026

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

Multimodal PPG-Based Arrhythmia Detection Using a CLIP-Initialized Multi-Task U-Net and LLM-Assisted Reporting.

Youngho Huh¹, Minhwan Noh², Dongwoo Ji¹

¹Department of Computer Science and Artificial Intelligence, Dongguk University, Seoul 04620, Republic of Korea.

Sensors (Basel, Switzerland)

|May 4, 2026

Summary

This summary is machine-generated.

This study introduces an advanced framework for detecting multiple arrhythmias using photoplethysmography (PPG) signals and clinical data. The system enhances accuracy and provides explainable reports for better cardiovascular monitoring.

Keywords:

arrhythmia detection clinical information contrastive learning explainable AI (XAI)heart rate variability (HRV)large language models (LLMs)photoplethysmography (PPG)retrieval-augmented generation (RAG)

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Last Updated: May 5, 2026

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Published on: July 20, 2022

Area of Science:

Biomedical Engineering
Cardiology
Artificial Intelligence

Background:

Photoplethysmography (PPG) offers non-invasive cardiovascular monitoring but current arrhythmia detection is limited to specific conditions like atrial fibrillation, lacks temporal localization, and deep learning models are often unexplainable.
Existing systems struggle with multi-class arrhythmia detection, precise segmentation of abnormal heart rhythms, and providing clinically interpretable insights.

Purpose of the Study:

To develop a comprehensive, integrated framework for multi-class arrhythmia detection, segmentation, and explainability using PPG waveforms, Heart Rate Variability (HRV), and clinical metadata.
To enhance the accuracy and clinical utility of PPG-based arrhythmia monitoring by incorporating explainability and detailed reporting.

Main Methods:

A CLIP-style contrastive learning module was developed to align PPG signals with clinical variables and textual rhythm descriptions using BioBERT.
A multitask U-Net architecture was employed for 4-class classification and 1D segmentation of PPG waveforms.
A Retrieval-Augmented Generation (RAG) pipeline with Gemini Flash LLMs was used to generate guideline-grounded diagnostic reports, integrated into a Streamlit web platform.

Main Results:

The framework significantly improved classification accuracy from 86.27% to 91.19%.
Segmentation performance showed a notable increase in Dice score from 0.5815 to 0.7167.
The system demonstrated enhanced explainability and provided detailed, guideline-grounded diagnostic reports.

Conclusions:

The developed system presents a robust, multimodal, and explainable approach for PPG-based arrhythmia monitoring.
This framework shows significant potential for real-world applications in cardiovascular health, improving upon existing limitations in detection scope, localization, and interpretability.